Ars Contexta is a Claude Code plugin that creates a personalized knowledge system from a conversation about how someone thinks and works, using connected Markdown files, note-processing skills, hooks, navigation maps, and templates. It is for people who want an owned, persistent second brain tailored to their domain. The catalogue entries are its generated skills, hooks, agent, and plugin workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agenticnotetaking/arscontexta --skill reflectgit clone --depth 1 https://github.com/agenticnotetaking/arscontextaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agenticnotetaking/arscontexta/reflect)<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/reflect"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/reflect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/reflect"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.06589 |
| Opus 5.5 | $0.00025 | $0.02636 |
| Sonnet 5.5 | $0.00012 | $0.01318 |
| Haiku 4.5 | $0.00006 | $0.00659 |
Grade C, and why
reflect scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf "$LOCKDIR" How it starts
The opening of the file, as written. The whole thing — 747 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Runtime Configuration (Step 0 — before any processing)
Read these files to configure domain-specific behavior:
-
ops/derivation-manifest.md— vocabulary mapping, platform hints- Use
vocabulary.notesfor the notes folder name - Use
vocabulary.note/vocabulary.note_pluralfor note type references - Use
vocabulary.reflectfor the process verb in output - Use
vocabulary.topic_map/vocabulary.topic_map_pluralfor MOC references - Use
vocabulary.cmd_reweavefor the next-phase suggestion - Use
vocabulary.inboxfor the inbox folder name
- Use
-
ops/config.yaml— processing depth, pipeline chainingprocessing.depth: deep | standard | quickprocessing.chaining: manual | suggested | automatic
If these files don't exist, use universal defaults.
Processing depth adaptation:
| Depth | Connection Behavior |
|---|---|
| deep | Full dual discovery (MOC + semantic search). Evaluate every candidate. Multiple passes. Synthesis opportunity detection. Bidirectional link evaluation for all connections. |
| standard | Dual discovery with top 5-10 candidates. Standard evaluation. Bidirectional check for strong connections only. |
| quick | Single pass — either MOC or semantic search. Accept obvious connections only. Skip synthesis detection. |
EXECUTE NOW
Target: $ARGUMENTS
Parse immediately:
- If target contains
[[note name]]or note name: find connections for that {vocabulary.note} - If target contains
--handoff: output RALPH HANDOFF block at end - If target is empty: check for recently created {vocabulary.note_plural} or ask which {vocabulary.note}
- If target is "recent" or "new": find connections for all {vocabulary.note_plural} created today
Execute these steps:
- Read the target {vocabulary.note} fully — understand its claim and context
- Throughout discovery: Capture which {vocabulary.topic_map_plural} you read, which queries you ran (with scores), which candidates you evaluated. This becomes the Discovery Trace — proving methodology was followed, not reconstructed.
- Run Phase 0 (index freshness check)
- Use dual discovery in parallel:
- Browse relevant {vocabulary.topic_map}(s) for related {vocabulary.note_plural}
- Run semantic search for conceptually related {vocabulary.note_plural}
- Evaluate each candidate: does a genuine connection exist? Can you articulate WHY?
- Add inline wiki-links where connections pass the articulation test
- Update relevant {vocabulary.topic_map}(s) with this {vocabulary.note}
- If task file in context: update the {vocabulary.reflect} section
- Report what was connected and why
- If
--handoffin target: output RALPH HANDOFF block
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 747 lines · 62 tokens per session scan C 98fef2d260f4
reflect is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,509 stars, last pushed 7mo ago), licensed MIT. It adds 62 tokens to every session and 6,589 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
llm-wiki
Build and maintain an LLM-curated knowledge base from papers, articles, transcripts, notes and project findings. Ingest sources into linked Markdown, answer questions with citations, lint or upgrade a wiki, and preserve useful synthesis across sessions and agents. Also capture verified task experience, consolidate…
migrate
Scan the project and migrate existing documentation into OKF format. Use when the user wants to populate the knowledge base from existing docs, README, schemas, API specs, runbooks, or any structured project knowledge. Trigger on "migrate to okf", "populate the knowledge base", "scan my docs", "convert docs to okf"…
claude-mega-brain
OKF knowledge navigation. ALWAYS use this skill the moment a block appears in session context — it means the project has documented concepts and you must consult them before answering questions about data, schemas, metrics, APIs, or systems. Also trigger when the user asks about a table, column, metric definition…
ingest
Create or update a single OKF concept file from raw input. Use when the user wants to document a specific table, metric, API, service, or concept in the knowledge base — even if they say "add this to the okf", "documenta essa tabela", "cria um conceito pra isso", "adiciona no knowledge base", "register this API", or…
init
Initialize OKF knowledge base files in the current project. Use when the user wants to start documenting their project for claude-mega-brain, or when the user says "init mega brain", "setup the knowledge base", "cria o okf", "inicializa o mega brain", "quero usar o mega brain", "criar a estrutura do knowledge base"…
query
Progressive retrieval from the Trove index. Use when the user asks what Trove knows about a subsystem, where code lives, or wants context assembled before editing. Runs architecture -> subsystem -> module -> symbol retrieval with a context budget.